System and method for predicting failure of components using temporal scoping of sensor data

ABSTRACT

An example method comprises receiving first historical data of a first time period and failure data, identifying at least some sensor data that was or potentially was generated during a first failure, removing the at least some sensor data to create filtered historical data, training a classification model using the filtered historical data, the classification model indicating at least one first classified state at a second period of time prior to the first failure indicated by the failure data, applying the classification model to second sensor data to identify a first potential failure state based on the at least one first classified state, the second sensor data being from a subsequent time period, generating an alert if the first potential failure state is identified based on at least a first subset of sensor signals generated during the subsequent time period, and providing the alert.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/231,160, filed Dec. 21, 2018 and entitled “System and Method forPredicting Failure of Components using Temporal Scoping of Sensor Data,”which claims priority to U.S. Provisional Patent Application No.62/623,713, filed on Jan. 30, 2018 and entitled “Methods and Apparatusto Predict Failure of Components using Temporal Scoping of Sensor Data,”both of which are incorporated in their entireties herein by reference.

FIELD OF THE INVENTION

Embodiments of the present invention(s) relate generally to forecastcongestion in electrical networks. In particular, the presentinvention(s) relate to forecasting congestion in electrical networks byreducing complexity of the network and utilizing historical data tocreate a model to forecast congestion to enable proactive solutions.

DESCRIPTION OF RELATED ART

When an asset of an electrical network is overloaded, the asset (andperhaps the network) is congested. A path (or a collection ofconductors) within the electrical network of a power company iscongested when one or multiple current-carrying elements (e.g.,conductors, transformers, AC lines, and the like) are operationallycarrying an amount of power which exceeds a specific threshold, for aspecific time-period.

Upon detection of existing congestion on an electrical network,transmission utilities typically undertake congestion mitigation steps,as opposed to congestion forecasting and proactive congestion managementor congestion avoidance. Congestion control approaches are, at present,rarely proactive, and utilities react to congestion using after-the-factmechanisms such as re-dispatch and controlling line flows by usingphase-shifting transformers. Utilities approach unknown futurecongestion by simply oversizing conductors which is not economical.Power companies do not adopt proactive congestion management for manyreasons including:

-   -   1. simulations require accurate internal and external network        models and wide-range of scenarios which are rarely available,        expensive to create, and computationally expensive;    -   2. planning approaches using AC power flow with Monte-Carlo        simulation adopt high dimensional models and are computationally        inefficient, thus rendering them inefficient for real-time        congestion forecasting;    -   3. presence of renewable energy generation exacerbates problem;    -   4. bottlenecks often exist on external network models and        acquisition of hyperlocal weather forecast information; and/or    -   5. data-only approaches that use line-flow forecasts trained on        only historical measurements are not accurate due to overfitting        on available features and the absence of the capability to        process network topology.

SUMMARY

An example nontransitory computer readable medium comprises executableinstructions, the executable instructions being executable by one ormore processors to perform a method, the method comprising receivingfirst historical data of a first time period and failure data of thefirst time period, the historical data including sensor data from one ormore sensors of a renewable energy asset, the failure data indicating atleast one first failure associated with the renewable energy assetduring the first time period, identifying at least some sensor data ofthe first historical data that was or potentially was generated duringthe first failure indicated by the failure data, removing the at leastsome sensor data from the first historical data to create filteredhistorical data, training a classification model using the filteredhistorical data, the classification model indicating at least one firstclassified state of the renewable energy asset at a second period oftime prior to the first failure indicated by the failure data, applyingthe classification model to second sensor data to identify a firstpotential failure state based on the at least one first classifiedstate, the second sensor data being from the one or more sensors of therenewable energy asset during a subsequent time period after the firsttime period, generating an alert if the first potential failure state isidentified based on at least a first subset of sensor signals generatedduring the subsequent time period, and providing the alert.

The renewable energy asset may be a wind turbine or a solar panel. Theone classified state of the renewable energy asset may be based on asubset of sensor readings generated prior to the failure and not all ofthe sensor readings of the first historical data generated prior to thefailure. In some embodiments, the second period of time is a desirednumber of days before a predicted failure associated with the firstpotential failure state. The alert may be provided at the desired numberof days.

In some embodiments, the method may further comprise training aclassification model using the filtered historical data, theclassification model indicating at least one second classified state ofthe renewable energy asset at the second period of time prior to a thirdfailure indicated by the failure data, applying the classification modelto second sensor data to identify a second potential failure state basedon the at least one second classified state, the second sensor databeing from the one or more sensors of the renewable energy asset duringthe subsequent time period after the first time period, wherein thefirst potential failure state is associated with a failure of a firstcomponent of the renewable energy asset and the second potential failurestate is associated with a failure of a second component of therenewable energy asset, and generating an alert if the second potentialfailure state is identified based on at least a second subset of sensorsignals generated during the subsequent time period, the first subset ofsensor signals not including at least one sensor signal of the secondsubset of sensor signals.

In various embodiments, identifying the at least some sensor data of thefirst historical data that was or potentially was generated during thefirst failure indicated by the failure data comprises scanning at leastsome of the first historical data and comparing at least some of thesensor data of the first historical data to a failure threshold toidentify at least some of the sensor data that was or potentially wasgenerated during the first failure. Identifying the at least some sensordata of the first historical data that was or potentially was generatedduring the first failure indicated by the failure data may compriseidentifying an amount of downtime based on the failure data, scanning atleast some of the first historical data, and comparing at least some ofthe sensor data of the first historical data to a failure threshold toidentify at least some of the sensor data that was or potentially wasgenerated during the first failure until the amount of sensor dataidentified as failure or potentially as failure based on the comparisonis at least equal to the amount of downtime, wherein removing the atleast some sensor data comprises removing the sensor data that was orpotentially was generated during the first failure.

The method may further comprise generating a probability score based ona probability of the second sensor data fitting into the at least oneclassified state, wherein generating the alert comprises comparing theprobability score to a threshold score. Providing the alert may compriseproviding a message indicating at least one component associated withthe second sensor data may potentially fail.

An example system may comprise at least one processor and memorycontaining instructions, the instructions being executable by the atleast one processor to: receive first historical data of a first timeperiod and failure data of the first time period, the historical dataincluding sensor data from one or more sensors of a renewable energyasset, the failure data indicating at least one first failure associatedwith the renewable energy asset during the first time period, identifyat least some sensor data of the first historical data that was orpotentially was generated during the first failure indicated by thefailure data, remove the at least some sensor data from the firsthistorical data to create filtered historical data, train aclassification model using the filtered historical data, theclassification model indicating at least one first classified state ofthe renewable energy asset at a second period of time prior to the firstfailure indicated by the failure data, apply the classification model tosecond sensor data to identify a first potential failure state based onthe at least one first classified state, the second sensor data beingfrom the one or more sensors of the renewable energy asset during asubsequent time period after the first time period, generate an alert ifthe first potential failure state is identified based on at least afirst subset of sensor signals generated during the subsequent timeperiod, and provide the alert.

An example method may comprise receiving first historical data of afirst time period and failure data of the first time period, thehistorical data including sensor data from one or more sensors of arenewable energy asset, the failure data indicating at least one firstfailure associated with the renewable energy asset during the first timeperiod, identifying at least some sensor data of the first historicaldata that was or potentially was generated during the first failureindicated by the failure data, removing the at least some sensor datafrom the first historical data to create filtered historical data,training a classification model using the filtered historical data, theclassification model indicating at least one first classified state ofthe renewable energy asset at a second period of time prior to the firstfailure indicated by the failure data, applying the classification modelto second sensor data to identify a first potential failure state basedon the at least one first classified state, the second sensor data beingfrom the one or more sensors of the renewable energy asset during asubsequent time period after the first time period, generating an alertif the first potential failure state is identified based on at least afirst subset of sensor signals generated during the subsequent timeperiod, and providing the alert.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a block diagram of an example of an electrical network insome embodiments.

FIG. 2 depicts components that often produce failures of wind turbinesand components that often produce failures in solar panel generators.

FIG. 3 depicts a common problem of detecting possible failure of one ormore components of a wind farm.

FIG. 4 depicts traditional failure prediction approaches of main shaftbearing failure in wind turbines as well as challenges.

FIG. 5 characterizes problems and propose solutions in some.

FIG. 6 is a block diagram of a component failure prediction system insome embodiments.

FIG. 7 describes an analytics driven approach that may be utilized bythe component failure prediction system in some embodiments.

FIG. 7 describes an analytics driven approach that may be utilized bythe component failure prediction system in some embodiments

FIG. 8 is a flowchart for predicting failures and/or potential failuresof renewable energy assets.

FIG. 9 is one example of removing sensor data from historical dataassociated with known or possible failure of a renewable energy asset insome embodiments.

FIG. 10 is another example of removing sensor data from historical dataassociated with known or possible failure of a renewable energy asset insome embodiments

FIG. 11 depicts analytic results including a case study using someembodiments.

FIG. 12 depicts analytic results including a case study using someembodiments.

FIG. 13 depicts a block diagram of an example computer system serveraccording to some embodiments.

DETAILED DESCRIPTION

In wind and solar generation industry, it may be crucial to forecastcomponent failures with lead time. Some embodiments described hereinutilize machine learning algorithms to build a sophisticated forecastingmodel based on multi-variate sensor data to forecast component failures.In various embodiments, systems and methods described herein overcomelimitations of the prior art including scalability, proactive warnings,and computational efficiency while providing improved accuracy.

Historically, various operational anomalies and historical failures,make model training difficult leading to inaccuracy in modeling. Someembodiments focus on a foundational method which define and scope inputdata for a particular type of failure forecasting problem. Based oninput data, such as a given set of sensor data, systems discussed hereinmay predict a component failure with a desired lead time. In variousembodiments, the methods and systems disclosed herein overcomespreviously existing models that overly weighted data at the time offailure in creating models which leads to inaccuracy andunpredictability. Systems and methods described herein may correct theproblem generated by the previous application of modeling and provideslead time to enable corrective action to take place prior to failure.

FIG. 1 depicts a block diagram 100 of an example of an electricalnetwork 100 in some embodiments. FIG. 1 includes an electrical network102, a component failure prediction system 104, a power system 106, incommunication over a communication network 108. The electrical network102 includes any number of transmission lines 110, renewable energysources 112, substations 114, and transformers 116. The electricalnetwork 102 may include any number of electrical assets includingprotective assets (e.g., relays or other circuits to protect one or moreassets), transmission assets (e.g., lines, or devices for delivering orreceiving power), and/or loads (e.g., residential houses, commercialbusinesses, and/or the like).

Components of the electrical network 102 such as the transmissionline(s) 110, the renewable energy source(s) 112, substation(s) 114,and/or transformer(s) 106 may inject energy or power (or assist in theinjection of energy or power) into the electrical network 102. Eachcomponent of the electrical network 102 may be represented by any numberof nodes in a network representation of the electrical network.Renewable energy sources 112 may include solar panels, wind turbines,and/or other forms of so called “green energy.” The electrical network102 may include a wide electrical network grid (e.g., with 40,000 assetsor more).

Each component of the electrical network 100 may represent one or moreelements of their respective components. For example, the transformer(s)116, as shown in FIG. 1 may represent any number of transformers whichmake up electrical network 100.

In some embodiments, the congestion mitigation system 104 providesreal-time congestion-forecasting on any number of path(s) (or pathequivalents) of the electrical network 102 to the power system 106. Thecongestion mitigation system 104 may identify a bus, path, and/orcombination of assets of the electrical network 102 which have asignificant influence on congestion as a path of interest. In variousembodiments, the congestion mitigation system 104 may compute power flowon the identified bus, path, and/or combination of assets of theelectrical network 102. The congestion mitigation system 104 may comparethe computed power flow to thresholds (e.g., which may be pre-set by thepower system 106) to assist in forecasting congestions.

In some embodiments, communication network 108 represents one or morecomputer networks (e.g., LAN, WAN, and/or the like). Communicationnetwork 108 may provide communication between any of the congestionmitigation system 104, the power system 106, and/or the electricalnetwork 102. In some implementations, communication network 108comprises computer devices, routers, cables, uses, and/or other networktopologies. In some embodiments, communication network 108 may be wiredand/or wireless. In various embodiments, communication network 108 maycomprise the Internet, one or more networks that may be public, private,IP-based, non-IP based, and so forth.

The component failure prediction system 104 may include any number ofdigital devices configured to forecast component failure of any numberof components and/or generators (e.g., wind turbine or solar powergenerator) of the renewable energy sources 112.

In various embodiments, the component failure prediction system104 mayreduce computational burden of forecasting failure of any number ofcomponents and/or generators by applying machine learning tools onhistorical data after the historical data has been scoped to removeoverfitting or other overly weighting features.

Using scoped historical data of component and/or device failure toforecast failure, the component failure prediction system104 mayforecast failure up through the next 3 hours, 6 hours, 8 hours, 12hours, 16 hours, 20 hours, 24 hours, 28 hours, 32 hours, 36 hours, 40hours, 44 hours, 48 hours, or more. It will be appreciated that thesystem is not limited to forecasting only the examples above, but may beany amount of time.

The power system 106 may include any number of digital devicesconfigured to control distribution and/or transmission of energy. Thepower system 106 may, in one example, be controlled by a power company,utility, and/or the like. A digital device is any device with at leastone processor and memory. Examples of systems, environments, and/orconfigurations that may be suitable for use with system include, but arenot limited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

A computer system may be described in the general context of computersystem executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types. Adigital device, such as a computer system, is further described withregard to FIG. 13 .

FIG. 2 depicts components that often produce failures of wind turbinesand components that often produce failures in solar panel generators.Failures in wind turbines often occur as a result of failures in a mainbearing 202, gearbox 204, generator 206, or anemometer 208. Failures insolar panel generators often occur as a result of failures in aninverter 210, panel degradation 212, and an IGBT 214.

A wind turbine has many potential components of failure. Differentsensors may provide different readings for one or more differentcomponents or combinations of components. Given the number of windturbines in a wind farm, the amount of data to be assessed may beuntenable using prior art methods. For example, data analytics systemsof the prior art do not scale, sensors provide too much data to beassessed by the prior art systems, and there is a lack of computationalcapacity in prior art systems to effectively assess data from wind farmsin a time sensitive manner. As a result, prior art systems are reactiveto existing failures rather than proactively providing reports orwarnings of potential future failure of one or more components.

For example, various embodiments regarding a wind turbine describedherein may identify potential failure of a main bearing 202, gearbox204, generator 206, or anemometer 208 of one or more wind turbines.Although many bearings may be utilized in a wind turbine (e.g., yaw andpitch bearings), the main shaft and gearbox of the wind turbine tend tobe the most problematic. For example, a main bearing 202 may fail due tohigh thrust load or may fail due to inadequate lubricant filmgeneration. Trends in redesign of a main shaft 202 and/or gearbox 204 ofa single wind turbine have been driven by unexpected failures in theseunits. The unplanned replacement of main-shaft bearing 202 can costoperators up to $450,000 and have an obvious impact on financialperformance.

Gearbox 204 failures are one of the largest sources of unplannedmaintenance costs. Gearbox 204 failures can be caused by design issues,manufacturing defects, deficiencies in the lubricant, excessive time atstandstill, high loading, and other reasons. There may be many differentmodes of gearbox 204 failure and, as such, it may be important toidentify the type of failure mode in addressing the failure. One mode ismicropitting which occurs when lubricant film between contactingsurfaces in a gearbox 204 is not thick enough. Macropitting occurs whencontact stress in a gear or breaking exceeds the fatigue strength of thematerial. Bending fatigue a failure mode that affects gear teeth andaxial cracking may occur in bearings of a gearbox; the cracks develop inthe axial direction, perpendicular to the direction of rolling.

The generator 206 typically converts the wind energy to electricalenergy. Failures often occur in bearings, stator, rotor, or the likewhich can lead to inconsistent voltage to total failure. Generator 206failure may be difficult to detect as a result of inconsistent weather,lack of motion, and/or partial failure of the anemometer 208.

The anemometer 208 uses moving parts as sensors. Anemometers 208 ofteninclude “cups” for wind speed measurements and a wind vane that uses a“vane tail” for measuring vector change, or wind direction. Freezingweather has caused the “cups” and “vane tail” to lock. If an anemometer208 under-reports wind speed because of a partial failure, there is anincrease in rotor acceleration that indicates a large amount of windenergy is not converted into electrical engineering. Rolling resistancein an anemometer 208 bearings typically increase over time until theyseize. Further, if the anemometer 208 is not accurate, the wind turbinewill not control blade pitch and rotor speed as needed. Poor orinaccurate measurements by the anemometer 208 will lead to incorrectadjustments and increased fatigue.

Similarly, various embodiments regarding a solar panel generatordescribed herein may identify potential failure of a inverter 210, solarpanel 212, and IGBT 214 in one or more solar panels of a solar farm.

A solar inverter 210 is an electrical converter to convert variabledirect current from a photovoltaic solar panel 212 into a utilityfrequency alternating current that can be fed to an electrical grid.Production loses are often attributable to poor performance of inverters210. Solar inventers 210 may overheat (caused by weather, use, orfailure of cooling systems) which can reduce production. Moisture maycause a short circuit which can cause complete or partial failure (e.g.,to a minimum “required” isolation level). Further, failure of the solarinverter 210 to restart after gird fault may require manual restartingof the equipment.

The panel 212 refers to the solar or photovoltaic panel. Thephotovoltaic panel 212 may degrade due to weather, poor cleaning,thermal cycling, damp heat, humidity freezing, and UV exposure. Thermalcycling can cause solder bond failures and cracks. Damp heat has beenassociated with delamination of encapsulants and corrosion of cells.Humidity freezing can cause junction box adhesion to fail. UV exposurecontributes to discoloration and backsheet degradation.

Solar inverters 210 often use insulated gate bipolar transistors (IGBT)214 for conversion of solar panel 212 output to AC voltage. Failures inthe IGBT 214 can be caused by fatigue, corrosion of metallizations,electromigration of metalizations, conductive filament formation, stressdriven diffusion voiding, and time dependent dielectric breakdown.

FIG. 3 depicts a common problem of detecting possible failure of one ormore components of a wind farm. As shown in FIG. 3 , there may be anynumber of wind turbines in a wind farm. Sensors of each wind turbine ina wind farm may generate its own data. As a result, there is a dump oftimeseries data which is overwhelming for prior art systems and priorart methods of assessment. As illustrated, monitoring hundreds of assetswith hundreds of sensor inputs is time-consuming and overwhelming foroperators to test. Existing prior art systems receive too muchtimeseries data to be effectively assessed in a scalable and/orcomputationally efficient manner. As a result, there is a conservativeand or reactive response to component and wind turbine failure. In otherwords, action is typically taken well after failure is detected or whenfailure is both immanent and unmistakable.

FIG. 4 depicts traditional failure prediction approaches of main shaftbearing failure in wind turbines as well as challenges. In this example,main shaft bearing failure may be caused by any number of components.For prior art analysis, challenges include identifying the correctmechanical systems model and nominal operating modes of that mechanicalsystem model.

Prior art approaches may also fail due to incorrect sensor data mapping.Mapping of sensor data may be based on observability and take intoaccount sensor dynamic range. In this example of the main shaft bearingfailure, sensor data regarding temperature, noise, and/or vibration maybe taken into account. For example, the sensor data related totemperature, noise, and/or vibration is observed against the backgroundof other sensor data readings, and the sensor dynamic range of eachindividual sensor or combination of sensors should be recognized.

Prior art systems often fail in tuning a failure detection threshold fora sensor reading. Prior art systems typically must identify modelspecific parameters and site-specific parameters. In this case thetemperature sensor data may indicate a high temperature warning relativeto some high temperature threshold. The noise data may be utilized forresonant frequency analysis to detect residents within a component ordevice. The vibration data may be assessed to determine excessivevibration relative to some vibration threshold.

Further early indication of failures in temperature, noise, vibration,or other failures, can be easily overlooked if it's nominal operatingmode is loosely defined by the prior art system.

FIG. 5 characterizes problems and propose solutions in some embodiments.The graph in FIG. 5 depicts sensor readings from multiple sensors over aperiod of time leading up to failure. The time before the failure isindicated as “lead time.” One goal is to improve lead time such thatalerts may be issued and/or actions taken to mitigate consequences offailure or avoid failure prior to that failure occurring.

In this example, the three sensors with sensor readings indicated in thegraph are part of a generator of the wind turbine. Each of the threesensors include an active power sensor, a generator winding temperaturesensor, and a rotor speed sensor. It will be appreciated that there maybe any number of sensors provide any number of data. A subset of sensorreadings may indicate a failure while the other sensors may not providedata related to the failure. As a result, in some embodiments, systemsand methods described herein may identify subsets of sensor readings(from a much larger set of sensor readings) that identify possiblefailure conditions.

Some embodiments of systems and methods described herein address aproblem of providing warnings a possible failure data before a minimumlead time given a list of historical component failures and readingsfrom sensors of a number of renewable energy devices (e.g., windturbines of a wind farm and or solar panel generators of a solar farm).Those systems and methods described herein may identify a list of latentvariables, and a set of timeseries features and may maximize (orimprove) failure detection accuracy.

In some embodiments, systems and methods described herein may chooserelevant SCADA (supervisory control and data acquisition) tags for oneor more relevant components. The systems may extract timeseries featuresand preset process multivariate inputs. Based on all or some of theforegoing, the system may learn rules (e.g., algorithms) to predictfailure of components.

Failure of systems and/or components after a gradual degradation of oneor more components may be very difficult to detect. Given time,historical data may normalize to the degraded performance of thosecomponents thereby making detection of ultimate failure more difficult.Further, in the prior art systems, overwhelming amounts of data from anynumber of components from any number of devices of a wind or solar farmmay make gradual changes in performance or sensor readings difficult todetect. In some embodiments, systems and methods described herein mayproactively predict failure of components or systems that have gradualdegradation.

In various embodiments, systems and methods described herein may receivesensor information from active power sensors, generator windingtemperature sensors, and rotor speed sensor, apply deep learning foranomaly detection, mine latent variables from the data after deeplearning, and apply supervised learning to the results to generate amodel for future systems. Example systems are discussed herein.

FIG. 6 is a block diagram of a component failure prediction system 104in some embodiments. The component failure prediction system 104 maypredict a component failure ahead of the actual failure. The componentfailure production system 104 may, in some embodiments, apply amulti-variate anomaly detection algorithm to sensors that are monitoringoperating conditions of any number of renewable assets (e.g., windturbines and or solar generators). The component failure productionsystem 104 may remove data associated with a past, actual failure of thesystem (e.g. of any number of components and or devices), thereforehighlighting subtle anomalies from normal operational conditions thatlead to actual failures.

The component failure production system 104 may fine-tune the model byapplying dimensionality reduction techniques to remove noise fromirrelevant sensor data (e.g., apply principal component analysis togenerate a model using linearly uncorrelated data and/or features fromthe data). For example, the component failure production system 104 mayutilize factor analysis to identify the importance of features withinthe data. The component failure production system 104 may also utilizeor more weighting vectors to highlight a portion or subset of sensordata that has high impact on the failure.

In some embodiments, the component failure production system 104 mayfurther scope the time series data by removing sensor data from theactual failure time period. In various embodiments, the componentfailure production system 104 may optionally utilize curated datafeatures to improve the accuracy of detection. Gearbox failuredetection, for example, may utilize temperature rise in the gearbox withregards to power generation, reactive power, and ambient temperature.

The component failure production system 104 may utilize a set ofclassification algorithms based on features that are fed from the rawsensor data, derived features, anomaly score, and fault score. The faultscore can be computed based on the dimensionality reduction techniquesuch as PCA and ICA. The Anomaly score can be computed based onmulti-variate anomaly detection.

FIG. 7 describes an analytics driven approach that may be utilized bythe component failure prediction system 104 in some embodiments. In someembodiments, the component failure prediction system 104 may receivehistorical data regarding renewable energy sources (e.g., wind turbines,solar panels, wind farms, solar farms, electrical grants, and/or thelike). The component failure prediction system 104 may break down thedata in order to identify important features and remove noise of pastfailures that may impact model building. The historical data may becurated to further identify important features and remove noise. Thecomponent failure prediction system 104 may further identify labels orcategories for machine learning. It will be appreciated that componentfailure prediction system 104 may, in some embodiments, identify labelsfor machine learning with or without data generation from other sources.

The component failure prediction system 104 may receive sensor dataregarding any number of components from any number of devices, such aswind turbines from a wind farm. The multivariate timeseries data incombination with the labels or categories for machine learning, mayassist for deep learning, latent variable mining, multivariate anomalydetection to assist and provide insights for component failureindication. These insights, which may predict upcoming failures, mayeffectively enable responses to upcoming failures with sufficient leadtime before failure impacts other components of energy generation.

It will be appreciated that identifying upcoming failures for any numberof components and renewable energy generation may become increasinglyimportant as sources of energy migrate to renewable energy. Failure ofone or more components may impact the grid significantly, and as aresult may put the electrical grid, or the legacy components of theelectrical grid, either under burden or cause them to fail completely.Further, failures of the electrical grid and/or failures of renewableenergy sources may threaten loss of property, business, or lifeparticularly at times where energy is critical (e.g., hospital systems,severe weather conditions such as heat waves, blizzards, or hurricanes,care for the sick, care for the elderly, and/or care of the young).

The component failure prediction system 104 may comprise thecommunication module 602, an anomaly based scoping module 604, a factoranalysis based scoping module 606, a training data preparation module608, a model training module 610, a model scoring module 612, anevaluation module 614, and a report and alert generation module 616. Thecomponent failure prediction system 104 may further comprise datastorage 618.

The communication module 602 may be configured to transmit and receivedata between two or more modules in component failure prediction system104. In some embodiments, the communication module 602 is configured toreceive information regarding assets of the electrical network 102(e.g., from the power system 106, sensors within components of theelectrical network 102 such as the renewable energy sources 112,third-party systems such as government entities, other utilities, and/orthe like).

The communication module 602 may be configured to receive historicaldata regarding electrical assets either individually or in combination(e.g., wind turbines, solar panels, windfarms, solar farms, componentsof devices, components of wind turbines, components of solar panels,substations 114, transformers 116, and/or transmission lines 110). Thecommunication module 602 may further receive sensor data from one ormore sensors of any number of electrical assets such as those describedabove.

The anomaly based scoping module 604 may receive historical dataincluding sensor data from any number of sensors of renewable energyassets. The sensor data may be from any number of sensors over apredetermined period of time. In some embodiments, the anomaly basedscoping module 604 normalizes and extracts derived features from thatsensor data. In one example, such features may include a ratio betweenactive power and windspeed measurement or temperature difference betweena control room and ambient temperatures.

The anomaly based scoping module 604 may receive any amount ofhistorical data regarding any number of wind turbines and/or windturbine farms. In one example, the historical data may include sensordata from any number of wind turbines in a wind farm over a length oftime (e.g., over one year). In another example historical data mayinclude sensor data from any number of wind turbines any number ofwindfarms over different lengths of time.

The sensor data may be from any number of sensor including sensors thatmeasure different conditions. For example, the sensor data may includedata from main bearing sensors, generator sensors, vibration sensors,anemometer sensors, and the like.

The anomaly based scoping module 604 may remove anomaly data points froma list of prior failure events during the time to perform 1st level datascoping. For example, the anomaly based scoping module 604 may receivehistorical data including sensor data of a wind farm that extendsthrough a first time period as well as failure data that indicatesfailures within the wind farm within that first time period. In someembodiments, if the failure data indicates the type of failure, time offailure, and the specific turbine(s) and/or type of failures, theanomaly based scoping module 604 may remove the historical data of thatfailure from the historical data. For example, the anomaly based scopingmodule 604 may remove sensor data from the historical data of sensorslinked to the failure (e.g., of the failing wind turbine and/or failingcomponents of the wind turbine) from the time the failure was detectedthrough to the time that failure correction was determined or detected.

The anomaly based scoping module 604 may subsequently rerun themultivariate anomaly model to set a second layer baseline to performprimary unsupervised learning. The anomaly based scoping module 604 maysubsequently store multivariate timeseries data along with anomalystatus for a predetermined period of the time when failures areexcluded. The anomaly based scoping module 604 may store themultivariate timeseries data within the data storage 618.

In various embodiments, the anomaly based scoping module 604 maygenerate different models for different types of failures.

The anomaly based scoping module 604 may train a multivariate anomalymodel (e.g., such as an isolation forest or random forest) to set abaseline for initial unsupervised learning.

The factor analysis based scoping module 606 may run a factor analysisof a combination of previous features to extract secondary data. Thefactor analysis based scoping module 606 can identify the top factors(e.g., most significant factors relative to other factors) thatcontribute into the historical failure of data. As a result, the factoranalysis based scoping module 606 may create a weighting vector for highimportance for particular modes of failures. The factor analysis basedscoping module 606 may perform initial scoping of sensor variable valuesby applying the weight vector.

The factor analysis based scoping module 606 may apply the weightingvector on some input sensor data of the historical data. The anomalybased scoping module 604 may exclude the time period from the list ofactual failure data for primary scoping of the data.

The training data preparation module 608 may build a predictive modelbased on the second layer baseline from the anomaly based scoping module604 and the primary scoping of data from the factor analysis basedscoping module 606. In some embodiments training data is prepared as atuple of anomaly score timeseries, fault score timeseries, an indicatorvariable showing particular mode of fault that occurred in the desiredtime period (e.g., 45 day lead time).

The model training module 610 may utilize classification algorithms formodel training. The classifications may include for example SVM,DeepLearning (such as CNN or CHAID). The training model input mayinclude balanced input such as, for example, scoped anomaly time series,scoped weighted sensor timeseries, and failure indications. In someembodiments the timeseries data is a matrix where the start time the endtime of the timeseries include maximum lead time, minimum lead time, andper desired time horizon (e.g., 45 days to 10 days before an event).

In some embodiments, the balanced input may require a minimum of failurehistory (e.g., 20%) which can be copied by subsampling the non-failurehistory and by boosting the failure history data (e.g., sliding windowfor the failure history data).

The model scoring module 612 may provide a probability of failure withinthe predefined lead time interval using the second layer baseline fromthe anomaly based scoping module 604, the primary scoping of data fromthe factor analysis based scoping module 606, and inputting the tuple ofscoped anomaly timeseries, scoped weighted timeseries, and failureindication.

The model scoring module 612 may provide a probability of failure withinthe predefined lead time interval.

Further computation of the failure can be accomplished by counting downthe time from the first date of the failure prediction.

The evaluation module 614 may be configured to apply a model generatedby the model training module 610 to sensor data received from the samewind turbine or renewable asset equipment that was used to produce thesensor data of the previous received historical data. In variousembodiments, the evaluation module 614 may compare new sensor data toclassified and/or categorized states identified by the model of themodel training module 610 to identify when sensor data indicates afailure state or a state associated with potential failure is reached.In some embodiments, the evaluation module 614 may score the likelihoodor confidence of such estate being reached. The evaluation module 614may compare the confidence or score against a threshold in order totrigger an alert or report. In another example, the evaluation module614 may compare the fit of sensor data to a failure state or stateassociate with potential failure that has been identified by the modelof the model training module 610 in order to trigger or not trigger analert or report. Further examples are discussed regarding FIG. 8 .

It will be appreciated that there may be different categorized statesidentified during model training. Each categorized state may beassociated with a different type of failure including mode of failure,component of failure, and/or the like.

The report and alert generation module 616 may generate an alert basedon the evaluation of the evaluation module 614. An alert may be amessage indicating a failure or type of failure as well as the specificrenewable energy asset (e.g., wind turbine or solar panel) that may beat risk of failure. Since the state identified by the model is a statethat is in advance of a potential failure, the alert should be triggeredin advance of the potential failure such that corrective action may takeplace. In some embodiments, different alerts may be generated based ondifferent possible failure and or different failure states. For example,some failure states may be more serious than others, as such more alertsand/or additional detailed alerts may be provided to a larger number ofdigital devices (e.g., cell phones, operators, utility companies,service computers, or the like) depending on the seriousness,significance, and/or imminence of failure.

In some embodiments, the report and alert generation module 616 maygenerate a report indicating any number of potential failures, theprobability of such failure, and the justification or reasoning based onthe model and the fit of previously identified states associated withfuture failure of components.

The data storage 618 may be any type of data storage including tablesdatabases or the like. The data store 618 may store models, historicaldata, current sensor data, states indicating possible future failure,alerts, reports, and/or the like.

A module may be hardware (e.g., circuitry and/or programmable chip),software, or a combination of hardware and software.

FIG. 8 is a flowchart for predicting failures and/or potential failuresof renewable energy assets. In the example of FIG. 8 , predictionfailures and/or potential failures of wind turbines is discussed. Instep 802, the anomaly based scoping module 604 performs anomaly basedscoping on historical data regarding a wind farm. For example, theanomaly based scoping module 604 may receive historical data includingsensor data from any number of sensors of any number of wind turbines inthe wind farm over a first period of time. Subsequently, the anomalybased scoping module 604 may filter out sensor data generated during aknown failure or generated during a statistical likelihood of failure.

In some embodiments, the anomaly based scoping module 604 may receivehistorical data generated by sensors during the first period of time andfailure data indicating failures during that first period of time. Thehistorical data may include sensor data regarding any number ofcomponents of any number of wind turbines. The historical data may bereceived from any number of sources including, for example, an operatorof a wind farm, utility, manufacturer, surface provider, or the like.Similarly, the failure data may be received from any number of sourcesincluding, for example, an operator of a wind farm, utility,manufacturer, surface provider, or the like. All or some of thehistorical data may be from the same sources or different sources thatprovide the failure data. In some embodiments, all or some of the sensordata of the historical data may be receive from any number of sensorsand/or a sensor aggregator.

The failure data may include different data indicating failure. In someembodiments, the failure data may only include a percentage of downtimeduring the first period of time of one or more wind turbines. In otherembodiments, the failure data may include additional information ordifferent information including, for example, indications when specificwind turbines failed and/or the type of component that failed (e.g.,generator failure).

If the failure data indicates the time of failure and the specific windturbines( ) that failed but not the type of failure (e.g., a type offailure condition or specific component that failed), the anomaly basedscoping module 604 may remove and/or filter sensor data generated bysensors of that particular specific wind turbine during the period ofthe detected failure. If the failure data indicates the time of failure,the specific wind turbine(s) that failed, and the type of failure, theanomaly based scoping module 604 may remove and/or filter sensor datagenerated by sensors related to that specific type of failure (e.g.,generator sensor data during a time of a generator fault of a particularwind turbine) of the particular specific wind turbine during the periodof the detected failure. Additionally, in some embodiments, the anomalybased scoping module 604 may remove and/or filter sensor data generatedby sensors of components that may be impacted by the failure (e.g.,vibration sensor data may be removed if the wind turbine blades stoppedmoving due to a bearing failure).

In various embodiments, the failure data only indicates a percentage oramount of downtime of the wind farm and/or any number of wind turbinesduring the first period of time of the historical data. The anomalybased scoping module 604 may review the data for sensor data indicatingfailures and possible failures. The anomaly based scoping module 604 mayidentify the strongest sensor data indicating failures of componentsand/or turbines in the historical data. In one example, the anomalybased scoping module 604 may compare different sensor data of differentcomponents to different component thresholds (e.g., data from generatorsensors is compared against a generator failure threshold to determinefailure).

In some embodiments, the anomaly based scoping module 604 tracks anamount of time where failure (e.g., failure or potential failure) isdetected in the sensor data of the historical period. If the amount oftime where failure is detected in the sensor data of the historicalperiod is equal to the amount of time/percentage of time identified inthe failure data, then the anomaly based scoping module 604 may removethat particular sensor data from the historical data. If the amount oftime where failure (e.g., failure or potential failure) is detected inthe sensor data is more than the amount of time/percentage of timeidentified in the failure data, then the anomaly based scoping module604 may remove, from the historical data, sensor data that includes thestrongest indications of failure relative to the other sensor data untilthe amount of time or percentage of amount of time of the first periodof time is satisfied. If the amount of time where failure (e.g., failureor potential failure) is detected in the sensor data is less than theamount of time/percentage of time identified in the failure data, thenthe anomaly based scoping module 604 may identify additional sensor datafrom the historical data (e.g., compare against higher thresholds) toremove until the amount of time or percentage of amount of time of thefirst period of time is satisfied.

It will be appreciated that additional sensor data may be removed fromthe historical data for any reason. For example, the anomaly basedscoping module 604 may remove additional sensor data immediately beforeand/or after failure. In some embodiments, the anomaly based scopingmodule 604 removes a predetermined amount of sensor data immediatelybefore and/or after a detected failure when it is unknown when thefailure specifically began and/or ended. In various embodiments, theanomaly based scoping module 604 removes a predetermined amount ofsensor data immediately before and/or after a detected failure to removedata that may unduly influence the analysis. In one example, the anomalybased scoping module 604 removes 10 seconds of sensor data before and/orafter a failure (e.g., 10 seconds of generator sensor data before and/orafter a generator failure is detected).

In various embodiments, the anomaly based scoping module 604 generatesfiltered historical data after removing sensor data from the originallyreceived (e.g., initial) historical data.

It will be appreciated that the anomaly based scoping module 604 mayfilter out different information depending on the failure data received.As such, the anomaly based scoping module 604 may filter out differentdata from different sets of historical time periods based on thedifferent sets of failure data.

FIG. 9 is one example of removing sensor data from historical dataassociated with known or possible failure of a renewable energy asset insome embodiments. As discussed herein, in step 902, the anomaly basedscoping module 604 receives sensor data (e.g., multivariate data) fromequipment during a first time period. In step 904, the anomaly basedscoping module 604 and/or the factor analysis based scoping module 606may optionally normalize and/or extract derived features from thereceived sensor data. In step 906, the factor analysis based scopingmodule 606 may train a multi-variate anomaly model to set a baseline. Invarious embodiments, the anomaly based scoping module 604 receivesfailure data and uses the failure data to set the baseline in order toidentify sensor data at the time of known or possible failure. In step908, the anomaly based scoping module 604 may remove anomaly data points(e.g., sensor data generated during failure) from list of prior failureevents during time period.

Optionally, in step 910, the anomaly based scoping module 604 may re-runmulti-variate anomaly model to set a second baseline. In step 912, theanomaly based scoping module 604 may optionally store multi-variatetime-series data along with anomaly status for period when known orpossible failures are excluded.

FIG. 10 is another example of removing sensor data from historical dataassociated with known or possible failure of a renewable energy asset insome embodiments. As discussed herein, in step 1002, the anomaly basedscoping module 604 and/or the factor analysis based scoping module 606may optionally run factor analysis of combination of previous featuresto extract secondary data. In step 1004, the anomaly based scopingmodule 604 may identify top factors contributing to the historicalfailure data (e.g., failures or potential failures identified in thefailure data). In step 1006, the anomaly based scoping module 604 maycreate a weighting factor for high importance for particular modes offailures to identify particularly relevant or significant failuresand/or modes of failures. In step 1008, the anomaly based scoping module604 may apply weighting vector on received sensor data from thehistorical data. In step 1010, the anomaly based scoping module 604excludes (e.g., removes) time series data from list of actual failuresor potential failures (e.g., from the failure data).

In step 804, the factor analysis based scoping module 606 may optionallyperform factor analysis based scoping on the filtered historical datafrom the anomaly based scoping module 604. As discussed herein, thefactor analysis based scoping module 606 may run a factor analysis of acombination of previous features to extract secondary data. The factoranalysis based scoping module 606 may identify the top factors (e.g.,most significant factors relative to other factors) that contribute tothe failure indicated in the failure data. As a result, the factoranalysis based scoping module 606 may create a weighting vector for highimportance for particular modes of failures. The factor analysis basedscoping module 606 may perform initial scoping of sensor variable valuesby applying the weight vector.

The factor analysis based scoping module 606 may apply the weightingvector on some sensor data of the filtered historical data. In variousembodiments, the factor analysis based scoping module 606 may applycomponent analysis (e.g., principal component analysis), samplevariance, or the like. The factor analysis based scoping module 606 mayapply mean value clustering. In some embodiments, the factor analysisbased scoping module 606 may assess data from a vibration sensor todetermine zero crossing data and aggregate the information over time.The factor analysis based scoping module 606 may identifycomponents/features of the data using different methods for differentdata of different sensors.

In step 806, the training data preparation module 608 may optionallybuild a predictive model based on the second layer baseline from theanomaly based scoping module 604 and the primary scoping of data fromthe factor analysis based scoping module 606. In some embodimentstraining data is prepared as a tuple of anomaly score timeseries, faultscore timeseries, an indicator variable showing particular mode of faultthat occurred in the desired time period (e.g., 45 day lead time).

In step 808, the model training module 610 may utilize classificationalgorithms for model training. The classifications may include forexample SVM, DeepLearning (such as CNN or CHAID). The training modelinput may include balanced input such as, for example, scoped anomalytime series, scoped weighted sensor timeseries, and failure indications.In some embodiments the timeseries data is a matrix where the start timethe end time of the timeseries include maximum lead time, minimum leadtime, and per desired time horizon (e.g., 45 days to 10 days before anevent). In various embodiments, model training module 610 utilizes arandom forest or isolation forest to classify different sensor data ofthe filtered historical data (e.g., the historical data without thesensor data at the time of failures that was previously removed).

In some embodiments, the model training module 610 may utilize therandom forest or isolation forest to classify or categorize a stateassociated with an identified failure from the failure data. Differentstates may be associated with different failures. In some embodiments,the model training module 610 classifies or categorizes states (e.g.,one or more sensor data readings) at a time (e.g., predetermined orundetermined time) before a known failure. The classified state mayindicate a combination of features and/or sensor readings that signal anupcoming failure or a possible upcoming failure. Different classifiedstates may include different features, different combination offeatures, sensors, and/or sensor readings based on the type of failure(e.g., the component that is failing) and/or how the component isfailing.

In step 810, the model scoring module 612 may provide a probability offailure within the predefined lead time interval (e.g., a state ofsensor readings at a time before a potential failure as discussedregarding the model training module 610). In various embodiments, adistribution may be generated based on the fit of one or more sensorreadings (e.g., the sensor data from one or more sensors) to the stateidentified by the model training module. This may generate a function tobe applied to later data.

In step 812, the evaluation module 614 may receive new sensor data afterthe models have been trained. The evaluation module 614 may assess allor some of the new sensor data using any number of the models generatedby the model training module 610 to determine if a state is present.

In some embodiments, the evaluation module 614 and/or the model scoringmodule 612 may apply the scoring function to determine a degree of fitor confidence of fit and/or a likelihood of the failure based on thestate of the sensor readings.

Based on a comparison of the state with a threshold and/or a score witha threshold, the report and alert generation module 616 may generate analert. The alert may be a message or other signal that may indicate thetype of failure that may occur, the wind turbine associated with thefailure, the lead time before the expected failure, and/or any otherinformation.

In step 616, the report and alert generation module 616 may generate areport indicating any number of possible failures, scores indicatinglikelihood of failure or confidence of sensor data fit to a previouslyidentified state, and/or the like.

It will be appreciated that the model may be periodically updated. Forexample a predetermined intervals and/or times, updated historical datafailure data may be received or previously received data may be used toretrain existing models or create new models. In one example, an oldersubset of previously received historical data may be removed andreplaced with recently received sensor data. Some data associated withknown failures (e.g., from new failure data) may be removed, new datafeatures may be optionally re-identified, and states re-identified sothat new models may be created with new categories to apply to new data.

Although these examples are directed to renewable energy assets, such aswind turbines and solar panel energy generators, it will be appreciatedthat any systems and methods used herein may be applied to any energyassets, renewable energy assets, or any hardware systems that generatedata associated with performance over a period of time.

FIG. 11 depicts analytic results including a case study using someembodiments. The generator alignment issue in graph 1102 depicts rawdata of a non-severe case. The Y axis of graph 1102 is in temperature(C) and the X axis is over time (e.g., March 2015-March 2016). The graphincludes sensor readings of temperatures of three different windings aswell as outdoor temperature (the outdoor temperature is consistentlybelow 40° C.). Graph 1104 is a generator anomaly index (e.g., generatedby the report in alert generation module 316) with an X axis indicatinga percentage of generator anomaly index and a Y axis of time (e.g.,March, 2015-March, 2016). In this example, there is an early indicationof failure on May 2 and the generator alignment problem is fixed on Oct.9, 2015 prior to failure. Note that during the failure, There is not ahigh value for the anomaly index showing that the component failureprediction system 104 is forecasting as opposed to detecting an existingcondition.

FIG. 12 depicts analytic results including a case study using someembodiments. The main bearing failure issue in graph 1202 depicts rawdata of a severe case. The Y axis of graph 1202 is in temperature (C)and the X axis is over time (e.g., February 2015-June 2015). The graph1202 includes sensor readings of temperatures of generator drive andtemperature, generator nondriving temperature, gearbox high-speed shaftDrive temperature, gearbox high-speed shaft nondriving temperature, andoutdoor temperature. In graph 1202, the outdoor temperature is below 40°C. Graph 1204 is a main bearing anomaly index (e.g., generated by thereport in alert generation module 316) with an X axis indicating apercentage of main bearing warning index and a Y axis of time (e.g.,February 2015-June 2015). In this example, there is an early indicationof failure on May 20 and the main bearing failure on Jun. 9, 2015. Aclear detection of the main bearing failure is shown in FIG. 1204 whilethe raw SCADA data fails to show the indication.

FIG. 13 depicts a block diagram of an example computer system server1300 according to some embodiments. Computer system server 1300 is shownin the form of a general-purpose computing device. Computer systemserver 1300 includes processor 1302, RAM 1304, communication interface1306, input/output device 1308, storage 1310, and a system bus 1312 thatcouples various system components including storage 1310 to processor1302.

System bus 1312 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnect (PCI) bus.

Computer system server 1300 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by congestion mitigation system 1304 and it includes bothvolatile and nonvolatile media, removable and non-removable media.

In some embodiments, processor 1302 is configured to execute executableinstructions (e.g., programs). In some embodiments, the processor 1004comprises circuitry or any processor capable of processing theexecutable instructions.

In some embodiments, RAM 1304 stores data. In various embodiments,working data is stored within RAM 1306. The data within RAM 1306 may becleared or ultimately transferred to storage 1310.

In some embodiments, communication interface 1306 is coupled to anetwork via communication interface 1306. Such communication can occurvia Input/Output (I/O) device 1308. Still yet, congestion mitigationsystem 1304 can communicate with one or more networks such as a localarea network (LAN), a general wide area network (WAN), and/or a publicnetwork (e.g., the Internet).

In some embodiments, input/output device 1308 is any device that inputsdata (e.g., mouse, keyboard, stylus) or outputs data (e.g., speaker,display, virtual reality headset).

In some embodiments, storage 1310 can include computer system readablemedia in the form of volatile memory, such as read only memory (ROM)and/or cache memory. Storage 1310 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage 1310 can be provided for readingfrom and writing to a non-removable, non-volatile magnetic media (notshown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CDROM, DVD-ROM or other optical media can be provided. Insuch instances, each can be connected to system bus 1312 by one or moredata media interfaces. As will be further depicted and described below,storage 1310 may include at least one program product having a set(e.g., at least one) of program modules that are configured to carry outthe functions of embodiments of the invention. In some embodiments, RAM1304 is found within storage 1310.

Program/utility, having a set (at least one) of program modules, such ascongestion mitigation system 1304, may be stored in storage 1310 by wayof example, and not limitation, as well as an operating system, one ormore application programs, other program modules, and program data. Eachof the operating system, one or more application programs, other programmodules, and program data or some combination thereof, may include animplementation of a networking environment. Program modules generallycarry out the functions and/or methodologies of embodiments of theinvention as described herein.

It should be understood that although not shown, other hardware and/orsoftware components could be used in conjunction with congestionmitigation system 1304. Examples, include, but are not limited to:microcode, device drivers, redundant processing units, and external diskdrive arrays, RAID systems, tape drives, and data archival storagesystems, etc.

Exemplary embodiments are described herein in detail with reference tothe accompanying drawings. However, the present disclosure can beimplemented in various manners, and thus should not be construed to belimited to the embodiments disclosed herein. On the contrary, thoseembodiments are provided for the thorough and complete understanding ofthe present disclosure, and completely conveying the scope of thepresent disclosure to those skilled in the art.

As will be appreciated by one skilled in the art, aspects of one or moreembodiments may be embodied as a system, method or computer programproduct. Accordingly, aspects may take the form of an entirely hardwareembodiment, an entirely software embodiment (including firmware,resident software, micro-code, etc.) or an embodiment combining softwareand hardware aspects that may all generally be referred to herein as a“circuit,” “module” or “system.” Furthermore, aspects may take the formof a computer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

1. A non-transitory computer readable medium comprising executableinstructions, the executable instructions being executable by one ormore processors to perform a method, the method comprising: receivingfirst historical data of a first time period and failure data of thefirst time period, the historical data including sensor data from one ormore sensors of a renewable energy asset, the failure data indicating atleast one first failure associated with the renewable energy assetduring the first time period; identifying at least some sensor data ofthe first historical data that was or potentially was generated duringthe first failure indicated by the failure data; removing the at leastsome sensor data from the first historical data to create filteredhistorical data; training a classification model using the filteredhistorical data, the classification model indicating at least one firstclassified state of the renewable energy asset at a second period oftime prior to the first failure indicated by the failure data; applyingthe classification model to second sensor data to identify a firstpotential failure state based on the at least one first classifiedstate, the second sensor data being from the one or more sensors of therenewable energy asset during a subsequent time period after the firsttime period; generating an alert if the first potential failure state isidentified based on at least a first subset of sensor signals generatedduring the subsequent time period; and providing the alert.